Strokes are either ischemic or hemorrhagic. Because the management of these subtypes is so different, the clinical distinction among the subtypes is one of the most important and urgentest steps in stroke management.
Intracerebral hemorrhage (ICH) is an acute and spontaneous extravasation of blood into the brain parenchyma. It accounts for 10-30% of all stroke admissions to hospital, and leads to catastrophic disability, morbidity, and a 6 month mortality of 30-50%.
The presentation of ICH is very variable and is indistinguishable from ischemic stroke on clinical grounds alone, making neuro-imaging absolutely necessary. CT has become part of the standard evaluation of ICH because of its wide availability, short scan time, and easy interpretation. Thus the first step for patients with stroke-like symptoms is to determine if there is hemorrhage from non-enhanced CT images.
Non-enhanced CT or simply CT is the recommended modality for the diagnosis of ICH. ICH exhibits hyperdensity, which can generally be recognized by experienced radiologists. Diagnosis of ICH from CT can be difficult when the lesion is small or is masked by normal structures or when the reader is inexperienced which is the case in emergency setting. An estimation of hematoma volume is extremely useful in predicting the patient's clinical course and directing management. The technical challenges for automatic delineation of ICH include 1) existence of non-ICH voxels with similar intensity to ICH, such as skull, calcification and streaks; 2) ICH itself exhibits large variation in grayscales, i.e., voxels around the boundary of an ICH region are darker and the grayscale is smaller when ICH enters ventricles; 3) models such as brain atlas are difficult to be directly incorporated due to the large voxel size and pathological nature of the clinical CT data; 4) the mass effect, deformation of brain tissues and spontaneous movement during scanning making the segmentation and anatomical localization of ICH hard.
Currently, CT has been widely adopted for differentiating ischemic or hemorrhagic stroke. However, report on quantifying hemorrhage from CT is scarce. Loncaric et al (Loncaric S, Dhawan A P, Broderick J, Brott T. 3-D image analysis of intra-cerebral brain hemorrhage from digitized CT films. Computer Methods and Programs in Biomedicine 1995; 46: 207-216) employed a semi-automatic method to determine ICH through 3 steps: determining the gray level ranges of background, brain tissues and skull by k-means method; choosing two fixed intensity thresholds (50 and 130) to remove skull through mathematical morphological operations; and selecting a seed point of ICH manually, together with the user-specified intensity threshold, to grow the ICH region. This method has several limitations including 1) user intervention is needed for setting key parameters (seed point and intensity thresholds), which may yield different ICH for different users; 2) mathematical morphological operations with fixed structuring element may not be able to remove the skull due to the ICH; and 3) fixed thresholds to grow ICH may under-segment or over-segment. Chan (Chan T, Huang H K. Effect of a computer-aided diagnosis system on clinicians' performance in detection of small acute intracranial hemorrhage on computed tomography. Academic Radiology 2008; 15 (3): 290-299) developed an image analysis system to delineate small acute intracranial hemorrhage, which cannot be extended for non-small ICH due to possible deformation and mass effect. However, it is shown by Chan and Huang (Chan T, Huang H K. Effect of a computer-aided diagnosis system on clinicians' performance in detection of small acute intracranial hemorrhage on computed tomography. Academic Radiology 2008; 15 (3): 290-299) that the system can improve the clinicians' performance in detecting acute ICH on CT. At present, the volume and anatomical localization of ICH are approximated manually from CT slices. This suffers from being tedious and laborious, requiring readers of CT to be an expert in neuron-radiology, and inability to control accuracy and to provide accurate 3D shape information of the ICH.